论文标题
Maniskill 2021的Silver-Bullet-3D：基于对象操纵的基于启发式规则的学习方法
Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation
论文作者
论文摘要
本文对我们的系统进行了概述和比较分析，该系统为Sapien Maniskill Challenge挑战2021的以下两条曲目设计： 没有相互作用轨迹：从预收到的演示轨迹中学习策略的无相互作用轨迹目标。我们研究了这两种基于模仿学习的方法，即使用经典的监督学习技术模仿观察到的行为，以及基于线的基于强化学习的方法。此外，通过基于变压器的网络利用对象和机器人臂的几何结构和纹理结构，以促进模仿学习。 无限制轨道：在此轨道中，我们设计了一种基于启发式规则的方法（HRM）来通过将任务分解为一系列子任务来触发高质量对象操纵。对于每个子任务，采用简单的基于规则的控制策略来预测可以应用于机器人臂的动作。 为了简化系统的实现，所有源代码和预训练的模型均可在\ url {https://github.com/caiqi/silver-bullet-3d/}上获得。
This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Interaction track targets for learning policies from pre-collected demonstration trajectories. We investigate both imitation learning-based approach, i.e., imitating the observed behavior using classical supervised learning techniques, and offline reinforcement learning-based approaches, for this track. Moreover, the geometry and texture structures of objects and robotic arms are exploited via Transformer-based networks to facilitate imitation learning. No Restriction Track: In this track, we design a Heuristic Rule-based Method (HRM) to trigger high-quality object manipulation by decomposing the task into a series of sub-tasks. For each sub-task, the simple rule-based controlling strategies are adopted to predict actions that can be applied to robotic arms. To ease the implementations of our systems, all the source codes and pre-trained models are available at \url{https://github.com/caiqi/Silver-Bullet-3D/}.
